Enhanced Gaussian Quantum Particle Swarm Optimization for the Clustering of Biomedical Data

The clustering is a crucial procedure that has several applications, including bioinformatics. It automatically extracts relevant knowledge from data by grouping homogeneous objects into the same group. Biomedical data clustering-focused metaheuristic algorithms are a field of current study due to their power to achieve global solutions instead of local ones. An intriguing metaheuristic technique that has been applied extensively in several fields of study is particle swarm optimization. In this paper, Gaussian Quantum Particle Swarm Optimization (GQPSO), inspired by quantum theory, is employed to resolve the clustering problem. First, it is adapted for the clustering of biomedical data. Then, the GQPSO search mechanism is reinforced by an acceleration strategy to increase the internal cluster cohesion. The experimental outcomes on well-known biomedical datasets are encouraging and support the recommended algorithm’s dominance over the cuckoo search method, genetic algorithm and both all typical and quantum particle swarm with regard to the quality of internal clustering.